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Efficient use of digital gadgets is essential in the modern digital age. Nevertheless, a digital divide remains since individuals with motor impairments sometimes have trouble utilizing conventional input methods like touch screens or mice. Electroencephalography (EEG)-based Motor Imagery Brain–Computer Interfaces (MI-BCIs) have evolved dramatically due to recent advances in deep learning. This paper presents a novel Brain–Computer Interface (BCI) system based on electroencephalography (EEG) that allows for a more intuitive and accurate operation of digital devices including interacting with digital information. By combining MI, which simulates motor actions without movement, with OSA, which involves spatial attention and visual focus, a multidimensional EEG paradigm is created, offering a comprehensive representation of brain activity. This innovative integration, coupled with advanced deep learning architectures tailored for EEG signal analysis, holds the potential to significantly enhance BCI performance by capturing a broader spectrum of cognitive processes and facilitating more precise control. The research translates user intentions into cursor movements by interpreting EEG signals collected during particular mental tasks. With this strategy, the gap in interactions between people with motor impairments and digital devices will be lessened, encouraging inclusion and improving their capacity for digital communication and self-expression.

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An EEG-Based Brain-Computer Interface Approach for Enhanced Interaction with Digital Devices

  • 会议论文

  • 发表日期：

    2025年

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  • Jubal Chandy Jacob,
  • Esther Daniel,
  • S. Durga,
  • S. Seetha

摘要

Efficient use of digital gadgets is essential in the modern digital age. Nevertheless, a digital divide remains since individuals with motor impairments sometimes have trouble utilizing conventional input methods like touch screens or mice. Electroencephalography (EEG)-based Motor Imagery Brain–Computer Interfaces (MI-BCIs) have evolved dramatically due to recent advances in deep learning. This paper presents a novel Brain–Computer Interface (BCI) system based on electroencephalography (EEG) that allows for a more intuitive and accurate operation of digital devices including interacting with digital information. By combining MI, which simulates motor actions without movement, with OSA, which involves spatial attention and visual focus, a multidimensional EEG paradigm is created, offering a comprehensive representation of brain activity. This innovative integration, coupled with advanced deep learning architectures tailored for EEG signal analysis, holds the potential to significantly enhance BCI performance by capturing a broader spectrum of cognitive processes and facilitating more precise control. The research translates user intentions into cursor movements by interpreting EEG signals collected during particular mental tasks. With this strategy, the gap in interactions between people with motor impairments and digital devices will be lessened, encouraging inclusion and improving their capacity for digital communication and self-expression.